Abstract
Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test (α = 0.05 level) for the first moment and the F test for the second moment (α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.
Highlights
Lakes are critical to the environment, biosphere, and human populations especially in sustaining the socioeconomic livelihoods of many rural and poor communities’ worldwide (Dubois et al 2018, Sayer et al 2018)
Based on the evaluation criteria, the results suggest that the best models for extreme lake levels were the generalized extreme value (GEV) and Gumbel distributions when fitted to the annual maximum series (AMS) series and the generalized Pareto (GPA) and exponential distributions when fitted to the peak over threshold (POT) series with a lake level threshold (u) of 476.1 meters Above Shire Valley Datum (m ASVD)
The results show that the covariates Indian Ocean Dipole (IOD) (AMS4) and Southern Oscillation Index (SOI) (AMS5) were not immediate key factors in the frequency analysis of AMS series of the lake levels, as both had higher values of Akaike information criteria (AIC), Bayesian information criteria (BIC), and NLL
Summary
Lakes are critical to the environment, biosphere, and human populations especially in sustaining the socioeconomic livelihoods of many rural and poor communities’ worldwide (Dubois et al 2018, Sayer et al 2018). 593 Page 2 of 23 and agriculture, reservoirs for hydropower generation, and transportation as well as providing a high recreation value for humans (Vainu and Terasmaa 2014, Kafumbata et al 2014) These lakes have not been spared from the impacts of climate change and variability (CCV) and human-induced effects such as land-cover and land-use changes, urbanization, changes in impervious surfaces and drainage network, deforestation, and mining (Bayazit 2015). Such impacts are well exhibited through lake level variations, which have profound primary influences on the productivity and structure of lake ecosystems (Gownaris et al 2018) thereby bringing a myriad of environmental and socioeconomic problems (Ye et al 2017). Other studies that have reported on changes in lake levels (i.e., either decreases or increases) in various regions include the following: Úbeda et al (2013) in the Ibera wetland of Argentina; Stefanidis et al (2016) in their study of two Environ Monit Assess (2020) 192: 593
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